Face recognition technology may be utilized to identify a person in various applications and contexts. Such applications and contexts may include, for example, computing systems using natural user interfaces, security systems, identity authentication systems, and the like. When used in these different applications and/or contexts, a face recognition system may encounter varying illumination conditions that pose challenges to the accuracy and reliability of the system. In one example, imaging sensors that capture facial image data may generate increasing noise as the illumination of the subject face decreases. As a result, in low illumination conditions the sensors may produce a lower quality image that may cause inaccurate results and inconsistent performance of the face recognition system.
In some prior approaches, noise reduction filters have been employed to reduce the amount of noise in the image prior to applying the face recognition technology. These approaches, however, typically set the filter parameters without regard to illumination conditions present when the image was captured. As a result, depending upon the illumination conditions, the filter may “over-correct” the region of interest removing small details, or “under-correct” the region of interest leaving the undesirable noise.
In other approaches, the direction of illumination incident on the subject face and/or the pattern of illumination that is present when the image is captured may be estimated. Such data may be used to preprocess the image prior to applying a face recognition application. These approaches, however, involve gathering and processing a significant amount of information, and are therefore computationally demanding and expensive.